Body condition and acoustic noise modify female responses to uni- and multimodal signals emitted by a male-mimicking robot frog

Mate choice is shaped by both intrinsic and extrinsic factors, including signal conspicuity, receiver body condition, and environmental properties. Multimodal signals are generally more conspicuous than unimodal ones and are expected to elicit stronger responses. However, variation in body condition and background noise modulate receivers’ responsiveness. We investigated how signal type (uni- or multimodal) interacts with body condition and background acoustic noise to influence female responses to male displays in the stream-breeding frog Crossodactylus schmidti. In a field experiment using a male-mimicking robot frog, females were exposed to acoustic (advertisement calls), visual (toe flags), and multimodal (advertisement calls + toe flags) stimuli, while their behavioral responses, body condition, and environmental acoustic noise were recorded. Females responded preferentially to multimodal stimuli over acoustic or visual ones. Those in poorer body condition were more responsive over..., , # Body condition and acoustic noise modify female responses to uni- and multimodal signals emitted by a male-mimicking robot frog

Dataset DOI: [10.5061/dryad.k98sf7mmj](https://doi.org/10.5061/dryad.k98sf7mmj)

## Description of the data and file structure

The data were used to test whether stimulus type, female body condition, and background acoustic noise influenced female responses to the robot frog. We used model selection with generalized linear mixed models (GLMMs) to analyze the *probability of female response* and random-effect (frailty) Cox Proportional Hazards (CPH) models to evaluate *latency to female response*. Female identity was included as a random factor in all models to account for repeated exposures of the same individual to different stimuli. The two continuous predictors, body condition and background acoustic noise, were standardized (i.e., centered to zero and scaled by standard deviation). 

The response variables used in the GLMMs were: (1) the probability of ...,

择偶决策受内在与外在因素共同塑造，涵盖信号显著性、接收者身体状态以及环境特性。多模态信号通常较单模态信号具备更高的显著性，且可引发更强的响应。然而，个体身体状态差异与背景噪声会调控接收者的响应水平。本研究以溪流繁殖蛙类施密特湍蛙（Crossodactylus schmidti）为对象，探究信号类型（单模态或多模态）与雌性个体身体状态、背景声学噪声的交互作用如何影响雌性对雄性求偶展示的响应。

我们借助仿雄性机器人蛙开展野外实验，为雌性个体分别呈现声学刺激（广告鸣叫（advertisement calls））、视觉刺激（趾部摆动（toe flags））以及多模态刺激（广告鸣叫+趾部摆动），同时记录其行为响应、身体状态与环境声学噪声水平。实验结果显示，相较于单模态声学或视觉刺激，雌性个体优先对多模态刺激产生响应。身体状态较差的雌性个体响应度更高……

# 身体状态与声学噪声调控仿雄性机器人蛙发出的单、多模态信号引发的雌性响应
数据集DOI: [10.5061/dryad.k98sf7mmj](https://doi.org/10.5061/dryad.k98sf7mmj)

## 数据与文件结构说明
本数据集用于验证刺激类型、雌性身体状态与背景声学噪声是否会影响雌性对机器人蛙的响应行为。我们采用广义线性混合模型（Generalized Linear Mixed Models, GLMM）开展模型选择，以分析**雌性响应概率**；同时采用带随机效应（脆弱性（frailty））的Cox比例风险模型（Cox Proportional Hazards, CPH）评估**雌性响应潜伏期**。所有模型均将雌性个体身份作为随机因子，以控制同一个体多次暴露于不同刺激所带来的重复测量偏差。两项连续预测变量——身体状态与背景声学噪声均经过标准化处理（即中心化至均值为0，并以标准差进行缩放）。

广义线性混合模型所采用的响应变量包括：(1) 响应概率……